Mapping active and inactive construction zones for autonomous driving

ABSTRACT

Aspects of the present disclosure relate to differentiating between active and inactive construction zones. In one example, this may include identifying a construction object associated with a construction zone. The identified construction object may be used to map the area of the construction zone. Detailed map information may then be used to classify the activity of the construction zone. The area of the construction zone and the classification may be added to the detailed map information. Subsequent to adding the construction zone and the classification to the detailed map information, the construction object (or another construction object) may be identified. The location of the construction object may be used to identify the construction zone and classification from the detailed map information. The classification of the classification may be used to operate a vehicle having an autonomous mode.

CROSS-REFERENCE TO RELATED APPLICATIONS

The present application is a continuation of U.S. patent applicationSer. No. 15/370,361, filed Dec. 6, 2016, which is a continuation of U.S.patent application Ser. No. 14/828,700, filed on Aug. 18, 2015, which isa divisional of U.S. patent application Ser. No. 13/859,990, now U.S.Pat. No. 9,141,107, filed Apr. 10, 2013, the disclosures of which areincorporated herein by reference.

BACKGROUND

Autonomous vehicles use various computing systems to aid in thetransport of passengers from one location to another. Some autonomousvehicles may require some initial input or continuous input from anoperator, such as a pilot, driver, or passenger. Other systems, forexample autopilot systems, may be used only when the system has beenengaged, which permits the operator to switch from a manual driving mode(where the operator exercises a high degree of control over the movementof the vehicle) to an autonomous driving mode (where the vehicleessentially drives itself) to modes that lie somewhere in between.

When operating in the autonomous mode, these vehicles may rely heavilyon pre-stored map data. Because of this, constructions zones may presentsignificant challenges to autonomous vehicles. In particular, theseareas may potentially change quickly, causing prior map data of theareas to be inaccurate or obsolete. Thus, detecting construction zonesis not only important, but also deciding how the vehicle should maneuverthrough these zones is important. Typically, upon identifying or comingupon a construction zone, an autonomous vehicle may automaticallytransfer control of the vehicle to the driver in order to ensure thesafety of the driver and any other passengers. However, this may befrustrating to the driver where the construction zone would not actuallypresent a driving challenge to the autonomous vehicle such as onhighways with long term construction projects.

BRIEF SUMMARY

Aspects of the disclosure provide a method. The method includesidentifying a first construction object associated with a constructionzone; mapping an area of the construction zone based on the identifiedconstruction object; classifying, by a processor, an activity type ofthe construction zone based on detailed map information; adding the areaof the construction zone and the classification to the detailed mapinformation; subsequent to adding the construction zone and theclassification to the detailed map information, identifying a secondconstruction object at a given location; identifying, from the detailedmap information, the area of the construction zone and theclassification based on the given location; and operating, at the areaof the construction zone, a vehicle having an autonomous driving modebased on the classification stored in the detailed map information.

In one example, classifying the activity type of the construction zoneincludes determining whether there have been any changes to the area ofthe construction zone from the detailed map information. In anotherexample, the activity type of the construction zone is classified asinactive when the features of the construction zone correspond to thefeatures of the detailed map information for that area. In this example,when the activity type of the construction zone is classified asinactive, operating the vehicle based on the classification includesoperating the vehicle in the autonomous driving mode through the area ofthe construction zone.

In another example, the activity type of the construction zone isclassified as active when the features of the construction zone indicatethat there have been changes to the roadway as compared to the detailedmap information. In this example, when the activity type of theconstruction zone is classified as active, operating the vehicle basedon the classification includes operating the vehicle in the manualdriving mode through the area of the construction zone.

In another example, operating the vehicle based on the classificationincludes determining whether to drive the vehicle in the autonomousdriving mode or a manual driving mode. In another example, the methodalso includes after operating the vehicle based on the classificationreclassifying the activity type of the construction zone and updatingthe classification of the construction zone in the detailed mapinformation based on the reclassifying. In this example, the method alsoincludes sending the updated classification to a remote computer. Inanother example, the second construction object is the firstconstruction object.

Another aspect of the disclosure provides a method. The method includesreceiving information identifying a construction zone and aclassification of an activity type of the construction zone; adding anarea of the construction zone and the classification to detailed mapinformation; subsequent to adding the area of the construction zone andthe classification to the detailed map information, identifying, by aprocessor, a construction object at a given location; and operating avehicle having an autonomous driving mode based on the classificationstored in the detailed map information.

In one example, the method also includes, after operating the vehiclebased on the classification, reclassifying the activity type of theconstruction zone and updating the classification of the constructionzone in the detailed map information based on the reclassifying. Inanother example, operating the vehicle based on the classificationincludes determining whether to drive the vehicle in the autonomousdriving mode or a manual driving mode. In another example, theinformation identifying the construction zone is further received withinstructions for controlling the driving behaviors of the vehicle in thearea of the construction zone and wherein operating the vehicle isfurther based on the instructions for controlling the driving behaviors.

A further aspect of the disclosure provides a system. The systemincludes memory storing detailed map information and a processor. Theprocessor is configured to identify a first construction objectassociated with a construction zone; map an area of the constructionzone based on the identified construction object; classify an activitytype of the construction zone based on the detailed map information; addthe area of the construction zone and the classification to the detailedmap information; subsequent to adding the construction zone and theclassification to the detailed map information, identify a secondconstruction object at a given location; identify, from the detailed mapinformation, the area of the construction zone and the classificationbased on the given location; and operate, at the area of theconstruction zone, a vehicle having an autonomous driving mode based onthe classification stored in the detailed map information.

In one example, classifying the activity type of the construction zoneincludes determining whether there have been any changes to the area ofthe construction zone from the detailed map information. In anotherexample, the activity type of the construction zone is classified asinactive when the features of the construction zone correspond to thefeatures of the detailed map information for that area. In anotherexample, operating the vehicle based on the classification includesdetermining whether to drive the vehicle in the autonomous driving modeor a manual driving mode. In another example, the processor is furtherconfigured to, after operating the vehicle based on the classification,reclassify the activity type of the construction zone and update theclassification of the construction zone in the detailed map informationbased on the reclassifying. In another example, the processor is furtherconfigured to generate instructions for controlling the drivingbehaviors of the vehicle in the area of the construction zone and addthe instructions for controlling the driving behaviors of the vehicle tothe detailed map information with the area of the construction zone andthe classification. In this example, operating at the area of theconstruction zone is further based on the instructions for controllingthe driving behaviors of the vehicle.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a functional diagram of a system in accordance with aspects ofthe disclosure.

FIG. 2 is an interior of an autonomous vehicle in accordance withaspects of the disclosure.

FIG. 3 is an exterior of an autonomous vehicle in accordance withaspects of the disclosure.

FIG. 4 is an example of a roadway in accordance with aspects of thedisclosure.

FIG. 5 is an example of detailed map information in accordance withaspects of the disclosure.

FIG. 6 is another example of detailed map information in accordance withaspects of the disclosure.

FIG. 7A is a pictorial diagram of a system in accordance with aspects ofthe disclosure.

FIG. 7B is a functional diagram of a system in accordance with aspectsof the disclosure.

FIG. 8 is another example of the roadway of FIG. 4 in accordance withaspects of the disclosure.

FIG. 9 is another example of the roadway of FIG. 4 in accordance withaspects of the disclosure.

FIG. 10 is another example of the roadway of FIG. 4 in accordance withaspects of the disclosure.

FIG. 11 is an example flow diagram in accordance with aspects of thedisclosure.

DETAILED DESCRIPTION

The disclosure relates generally to mapping and classifying constructionzones in order to improve safety and efficiency of autonomous vehicles.As an example, a first vehicle with access to a detailed map may bedriven along a roadway in an autonomous or manual driving mode. Thefirst vehicle may use its sensors to detect a construction zone, forexample, by identifying a construction object such as a road work sign,cone, barrel, fence, construction vehicle, flare, or other itemscommonly associated with construction zones. Where the vehicle is beingdriven in an autonomous mode, upon identifying the construction object,the vehicle may switch to a manual mode or a very cautious driving mode.As the first vehicle is driving (or after it has driven) through theconstruction zone, the first vehicle may map the area of theconstruction zone and incorporate this construction zone informationinto the detailed map.

By comparing the detailed map information to the features of the roadwaydetected in the construction zone, such as the positioning of lanelines, whether lanes are closed, the presence and location of a newk-rail, etc., the first vehicle may determine whether the constructionzone is active or inactive. In this regard, if changes to theaforementioned features are identified, then the construction zone maybe classified as active. If no changes (or very minor changes) areidentified, then the construction zone may be classified as inactive.This information may be associated with the construction zoneinformation and incorporated into the detailed map. This information mayalso be shared with other vehicles in order to update their own detailedmaps.

A second vehicle (or the first vehicle at a later time), may detect theconstruction zone by detecting a construction object such as road worksigns, cones, barrels, fences, construction vehicles, flares, or otheritems commonly associated with construction zones. The second vehiclemay then access the detailed map to determine whether the location ofthe detected construction object corresponds to a construction zoneincluded and if so, whether the status of the construction zoneassociated with the detected object is identified as active or inactive.If the construction zone is active, the second vehicle may user a higherlevel of caution, by driving very slowly and perhaps transitioning froman autonomous to a manual mode. If the construction zone is inactive,the vehicle may continue to drive normally based on the information inthe map. If the detected object is not in the map, the second vehiclemay be driven under the higher level of caution and may add the detectedobject to the map. In addition, the second vehicle may also reclassifythe construction zone.

As shown in FIG. 1, an autonomous driving system 100 in may include avehicle 101 with various components. While certain aspects of thedisclosure are particularly useful in connection with specific types ofvehicles, the vehicle may be any type of vehicle including, but notlimited to, cars, trucks, motorcycles, busses, boats, airplanes,helicopters, lawnmowers, recreational vehicles, amusement park vehicles,farm equipment, construction equipment, trams, golf carts, trains, andtrolleys. The vehicle may have one or more computers, such as computer110 containing a processor 120, memory 130 and other componentstypically present in general purpose computers.

The memory 130 stores information accessible by processor 120, includinginstructions 132 and data 134 that may be executed or otherwise used bythe processor 120. The memory 130 may be of any type capable of storinginformation accessible by the processor, including a computer-readablemedium, or other medium that stores data that may be read with the aidof an electronic device, such as a hard-drive, memory card, ROM, RAM,DVD or other optical disks, as well as other write-capable and read-onlymemories. Systems and methods may include different combinations of theforegoing, whereby different portions of the instructions and data arestored on different types of media.

The instructions 132 may be any set of instructions to be executeddirectly (such as machine code) or indirectly (such as scripts) by theprocessor. For example, the instructions may be stored as computer codeon the computer-readable medium. In that regard, the terms“instructions” and “programs” may be used interchangeably herein. Theinstructions may be stored in object code format for direct processingby the processor, or in any other computer language including scripts orcollections of independent source code modules that are interpreted ondemand or compiled in advance. Functions, methods and routines of theinstructions are explained in more detail below.

The data 134 may be retrieved, stored or modified by processor 120 inaccordance with the instructions 132. For instance, although the claimedsubject matter is not limited by any particular data structure, the datamay be stored in computer registers, in a relational database as a tablehaving a plurality of different fields and records, XML documents orflat files. The data may also be formatted in any computer-readableformat. By further way of example only, image data may be stored asbitmaps comprised of grids of pixels that are stored in accordance withformats that are compressed or uncompressed, lossless (e.g., BMP) orlossy (e.g., JPEG), and bitmap or vector-based (e.g., SVG), as well ascomputer instructions for drawing graphics. The data may comprise anyinformation sufficient to identify the relevant information, such asnumbers, descriptive text, proprietary codes, references to data storedin other areas of the same memory or different memories (including othernetwork locations) or information that is used by a function tocalculate the relevant data.

The processor 120 may be any conventional processor, such ascommercially available CPUs. Alternatively, the processor may be adedicated device such as an ASIC or other hardware-based processor.Although FIG. 1 functionally illustrates the processor, memory, andother elements of computer 110 as being within the same block, it willbe understood by those of ordinary skill in the art that the processor,computer, or memory may actually comprise multiple processors,computers, or memories that may or may not be stored within the samephysical housing. For example, memory may be a hard drive or otherstorage media located in a housing different from that of computer 110.Accordingly, references to a processor or computer will be understood toinclude references to a collection of processors or computers ormemories that may or may not operate in parallel. Rather than using asingle processor to perform the steps described herein, some of thecomponents, such as steering components and deceleration components, mayeach have their own processor that only performs calculations related tothe component's specific function.

In various aspects described herein, the processor may be located remotefrom the vehicle and communicate with the vehicle wirelessly. In otheraspects, some of the processes described herein are executed on aprocessor disposed within the vehicle and others by a remote processor,including taking the steps necessary to execute a single maneuver.

Computer 110 may include all of the components normally used inconnection with a computer such as a central processing unit (CPU),memory (e.g., RAM and internal hard drives) storing data 134 andinstructions such as a web browser, an electronic display 152 (e.g., amonitor having a screen, a small LCD touch-screen or any otherelectrical device that is operable to display information), user input150 (e.g., a mouse, keyboard, touch screen and/or microphone), as wellas various sensors (e.g., a video camera) for gathering explicit (e.g.,a gesture) or implicit (e.g., “the person is asleep”) information aboutthe states and desires of a person.

In one example, computer 110 may be an autonomous driving computingsystem incorporated into vehicle 101. FIG. 2 depicts an exemplary designof the interior of an autonomous vehicle. The autonomous vehicle mayinclude all of the features of a non-autonomous vehicle, for example: asteering apparatus, such as steering wheel 210; a navigation displayapparatus, such as navigation display 215 (which may be a part ofelectronic display 152); and a gear selector apparatus, such as gearshifter 220. The vehicle may also have various user input devices 140 inaddition to the foregoing, such as touch screen 217 (which may be a partof electronic display 152), or button inputs 219, for activating ordeactivating one or more autonomous driving modes and for enabling adriver or passenger 290 to provide information, such as a navigationdestination, to the autonomous driving computer 110.

The autonomous driving computing system may be capable of communicatingwith various components of the vehicle. For example, returning to FIG.1, computer 110 may be in communication with the vehicle's centralprocessor 160 and may send and receive information from the varioussystems of vehicle 101, for example the braking system 180, accelerationsystem 182, signaling system 184, and navigation system 186 in order tocontrol the movement, speed, etc. of vehicle 101. In one example, thevehicle's central processor 160 may perform all of the functions of acentral processor in a non-autonomous computer. In another example,processor 120 and 160 may comprise a single processing device ormultiple processing devices operating in parallel.

In addition, when engaged, computer 110 may control some or all of thesefunctions of vehicle 101. Thus vehicle 101 may have various manual,semiautonomous, or autonomous driving modes as described above. It willbe understood that although various systems and computer 110 are shownwithin vehicle 101, these elements may be external to vehicle 101 orphysically separated by large distances.

The vehicle may also include a geographic position component 144 incommunication with computer 110 for determining the geographic locationof the device. For example, the position component may include a GPSreceiver to determine the device's latitude, longitude and/or altitudeposition. Other location systems such as laser-based localizationsystems, inertial-aided GPS, or camera-based localization may also beused to identify the location of the vehicle. The location of thevehicle may include an absolute geographical location, such as latitude,longitude, and altitude as well as relative location information, suchas location relative to other cars immediately around it, which canoften be determined with better accuracy than absolute geographicallocation.

The vehicle may also include other devices in communication withcomputer 110, such as an accelerometer, gyroscope or anotherdirection/speed detection device 146 to determine the direction andspeed of the vehicle or changes thereto. By way of example only,acceleration device 146 may determine its pitch, yaw or roll (or changesthereto) relative to the direction of gravity or a plane perpendicularthereto. The device may also track increases or decreases in speed andthe direction of such changes. The device's provision of location andorientation data as set forth herein may be provided automatically tothe user, computer 110, other computers and combinations of theforegoing.

The computer 110 may control the direction and speed of the vehicle bycontrolling various components. By way of example, if the vehicle isoperating in a completely autonomous driving mode, computer 110 maycause the vehicle to accelerate (e.g., by increasing fuel or otherenergy provided to the engine), decelerate (e.g., by decreasing the fuelsupplied to the engine or by applying brakes) and change direction(e.g., by turning the front two wheels).

The vehicle may also include components for detecting objects externalto the vehicle such as other vehicles, obstacles in the roadway, trafficsignals, signs, trees, etc. The detection system 154 may include lasers,sonar, radar, cameras or any other detection devices which record datawhich may be processed by computer 110. For example, if the vehicle is asmall passenger vehicle, the car may include a laser mounted on the roofor other convenient location.

As shown in FIG. 3, vehicle 101 may include a small passenger vehiclehaving lasers 310 and 311, mounted on the front and top of the vehicle,respectively. Laser 310 may have a range of approximately 150 meters, athirty degree vertical field of view, and approximately a thirty degreehorizontal field of view. Laser 311 may have a range of approximately50-80 meters, a thirty degree vertical field of view, and a 360 degreehorizontal field of view. The lasers may provide the vehicle with rangeand intensity information which the computer may use to identify thelocation and distance of various objects. In one aspect, the lasers maymeasure the distance between the vehicle and the object surfaces facingthe vehicle by spinning on its axis and changing its pitch.

The vehicle may also include various radar detection units, such asthose used for adaptive cruise control systems. The radar detectionunits may be located on the front and back of the car as well as oneither side of the front bumper. As shown in the example of FIG. 3,vehicle 101 includes radar detection units 320-323 located on the side(only one side being shown), front and rear of the vehicle. Each ofthese radar detection units may have a range of approximately 200 metersfor an approximately 18 degree field of view as well as a range ofapproximately 60 meters for an approximately 56 degree field of view.

In another example, a variety of cameras may be mounted on the vehicle.The cameras may be mounted at predetermined distances so that theparallax from the images of 2 or more cameras may be used to compute thedistance to various objects. As shown in FIG. 3, vehicle 101 may include2 cameras 330-331 mounted under a windshield 340 near the rear viewmirror (not shown). Camera 330 may include a range of approximately 200meters and an approximately 30 degree horizontal field of view, whilecamera 331 may include a range of approximately 100 meters and anapproximately 60 degree horizontal field of view.

In addition to the sensors described above, the computer may also useinput from other sensors and features typical to non-autonomousvehicles. For example, these other sensors and features may include tirepressure sensors, engine temperature sensors, brake heat sensors, breakpad status sensors, tire tread sensors, fuel sensors, oil level andquality sensors, air quality sensors (for detecting temperature,humidity, or particulates in the air), door sensors, lights, wipers,etc. This information may be provided directly from these sensors andfeatures or via the vehicle's central processor 160.

Many of these sensors provide data that is processed by the computer inreal-time, that is, the sensors may continuously update their output toreflect the environment being sensed at or over a range of time, andcontinuously or as-demanded provide that updated output to the computerso that the computer can determine whether the vehicle's then-currentdirection or speed should be modified in response to the sensedenvironment.

In addition to processing data provided by the various sensors, thecomputer may rely on environmental data that was obtained at a previouspoint in time and is expected to persist regardless of the vehicle'spresence in the environment. For example, returning to FIG. 1, data 134may include detailed map information 136, e.g., highly detailed mapsidentifying the shape and elevation of roadways, lane lines,intersections, crosswalks, speed limits, traffic signals, buildings,signs, real time traffic information, vegetation, or other such objectsand information. For example, the map information may include explicitspeed limit information associated with various roadway segments. Thespeed limit data may be entered manually or scanned from previouslytaken images of a speed limit sign using, for example, optical-characterrecognition.

FIG. 4 is an example of a highway 400. In this example, highway 400includes 3 northbound lanes 410-412 and 3 southbound lanes 420-22defined by broken lane lines 430-33 and solid lane lines 440-43. Highway400 also includes shoulders 450-51 defined between solid lane line 440and barrier 460 and solid lane line 441 and barrier 461, respectively.Between the northbound and southbound lanes, highway 400 includes amedian 470 which defines shoulders 452 and 453 with lane lines 442 and441, respectively.

FIG. 5 is an example of detailed map information 500 for highway 400.Detailed map information 500 may be a portion of the detailed mapinformation 136 and may include data indicating the location andorientation of the various features of highway 400. For example,detailed map information 500 includes northbound lane data 510-512identifying northbound lanes 410-412 as well as southbound lane data520-522 identifying southbound lanes 420-22. Detailed map information500 also includes broken lane line data 530-33 and solid lane lines540-43 representing broken lane lines 430-33 and solid lane lines440-43. Shoulders 450-53 are also represented by shoulder data 550-553.Barriers 460-61 are represented by barrier data 560-61, and median 470is represented by median data 570.

The detailed map information may also include information regardingconstruction zone areas. This may include location information definingthe boundaries of construction zone areas. For example, FIG. 6 is anexample of map information 500 including construction zone areas 610 and620.

These construction zone areas may also be associated with aclassification based on the most recent analysis of the activity of theconstruction zone. As an example, a construction zone area may beclassified as active or inactive depending upon the characteristics ofthe features in the construction zone area. Referring to FIG. 6construction zone area 610 includes an active designation 630 andconstruction zone area 620 includes an inactive designation 640.

Although the detailed map information 136 is depicted herein as animage-based map, the map information need not be entirely image based(for example, raster). The map information may include one or moreroadgraphs or graph networks of information such as roads, lanes,intersections, and the connections between these features. Each featuremay be stored as graph data and may be associated with information suchas a geographic location whether or not it is linked to other relatedfeatures. For example, a stop sign may be linked to a road and anintersection. In some examples, the associated data may includegrid-based indices of a roadgraph to promote efficient lookup of certainroadgraph features.

Computer 110 may also receive or transfer information to and from othercomputers. For example, the map information stored by computer 110 maybe received or transferred from other computers and/or the sensor datacollected from the sensors of vehicle 101 may be transferred to anothercomputer for processing as described herein. As shown in FIGS. 7A and7B, data from computer 110, such as sensor information, may betransmitted via a network to a central processing computer 720 forfurther processing. Similarly, data from computer 720, such as softwareupdates or weather information as described below, may be transmittedvia the network to computer 110 or other similar computers of othervehicles having an autonomous driving mode.

The network, and intervening nodes, may comprise various configurationsand protocols including the Internet, World Wide Web, intranets, virtualprivate networks, wide area networks, local networks, private networksusing communication protocols proprietary to one or more companies,Ethernet, WiFi and HTTP, and various combinations of the foregoing. Suchcommunication may be facilitated by any device capable of transmittingdata to and from other computers, such as modems and wirelessinterfaces. In another example, data may be transferred by storing it onmemory which may be accessed by or connected to computers 110 and 720.

In one example, computer 720 may comprise a server having a plurality ofcomputers, e.g., a load balanced server farm, that exchange informationwith different nodes of a network for the purpose of receiving,processing and transmitting the data to and from computer 110. Theserver may be configured similarly to the computer 110, with a processor730, memory 740, instructions 750, and data 760.

In addition to the operations described above and illustrated in thefigures, various operations will now be described. It should beunderstood that the following operations do not have to be performed inthe precise order described below. Rather, various steps can be handledin a different order or simultaneously, and steps may also be added oromitted.

As an example, a first vehicle may be driven along a roadway. This firstvehicle may include vehicle 101 and may be operating in an autonomousdriving mode, a manual driving mode, or a mode that lies somewherebetween these. Alternatively, this first vehicle may also be anon-autonomous vehicle driven simply for the purpose of collectingsensor information about the environment.

FIG. 8 is an example of vehicle 101 driving along North-bound lane 410of roadway 400 where construction vehicles 840 and 850 are repaving andpainting lane lines of the roadway. In this example, vehicle 101 isapproaching a construction zone as indicated by the various constructionobjects 810, 820, 830, 840, 850, and 860. This example also includesconstruction objects 870, 880, and 890 indicating that shoulder 453 isclosed.

As the vehicle is driven, sensors may collect information about thevehicle's environment. The information from the vehicle's sensors may beused to detect construction zones. For example, sensor information maybe used to identify construction objects such as road work signs, cones,barrels, fences, construction vehicles, flares, or other items commonlyassociated with construction zones. For example, computer 110 of vehicle101 may detect construction objects 810, 820, 830, 840, 850, and 860 inNorth-bound lanes 410-412. In another example, a vehicle driving inSouth-bound lanes 420-422 may detect construction objects 870, 880, and890.

These construction objects may be identified using any number oftechniques such as by comparison of sensor data to the detailed mapinformation, image matching, probability filters, image templates, etc.For example, techniques described in U.S. Pat. No. 8,195,394, U.S.patent application Ser. No. 13/361,083, entitled VEHICLE CONTROL BASEDON PERCEPTION UNCERTAINTY, and/or U.S. patent application Ser. No.13/799,829, entitled HIGH-ACCURACY REAL-TIME ROAD SIGN DETECTION FROMIMAGES, may be used to identify construction objects.

In some examples, where the vehicle is being driven in an autonomousmode, upon identifying a construction object, the vehicle mayautomatically warn the driver and switch to a manual mode.Alternatively, the vehicle may warn the driver and computer 110 mayswitch to a very cautious driving mode, for example, by driving moreslowly, increasing the distance between the vehicle and other objects inthe roadway, etc.

Once a construction object is detected, the construction area may bemapped. For example, computer 110 may detect any number of additionalconstruction objects or determine when the features of the roadway onceagain correspond to the detailed map information. Thus, a startingpoint, based on the location of the first construction object, and anending point may be identified based on the location of the lastidentified construction object. The area along the roadway between thesepoints may be the construction zone. In some examples, some buffer areamay be added to the construction area by adding some distance beyond thelast detected construction object.

As an example, computer 110 may detect construction object 810 and beginmapping a construction area. Once the last construction object has beendetected, such as construction object 860, computer 110 may use thisinformation to define a construction zone area 910 of FIG. 9. Similarly,a vehicle detecting construction objects 870 and 890 may define aconstruction zone 1010 of FIG. 10.

In addition to mapping the construction zone, it may also be classifiedby the system. As an example, a construction zone may be classified asactive or inactive based on upon the characteristics of the constructionzones. This classification may be accomplished by comparing the detailedmap information to the features, other than those used to identify theconstruction zone, of the roadway detected in the construction zone.This may include comparing the positioning of lane lines, whether lanesare closed, the presence and location of a new k-rail or other barrier,etc. If any differences or changes to the aforementioned features areidentified, then the construction zone may be classified as active. Ifno changes or very minor changes, are identified, then the constructionzone may be classified as inactive. Examples of minor changes mayinclude those that would not affect the autonomous driving, such aschanges outside of the road lanes including shoulder work or moving adivider in a way that would not affect the vehicle's localizationsystems, or a slight lane shift that is small enough that driving withinthe previously mapped lane from the detailed map information would stillbe safe or comfortable to the driver.

Referring to the examples of FIGS. 8 and 9, construction zone 910 may beclassified as active as portions of lane lines 441 and 431 do not appear(they have been paved over) and thus would be different from thedetailed map information. In the example of FIGS. 8 and 10, constructionzone 1010 may be classified as inactive as there would not be anydifferences from the detailed map information other than the appearanceof the construction objects.

The aforementioned identifications and classifications of constructionzones may be done at a later time by a computer other than that of thevehicle which collected the sensor information. For example, a computersuch as server 720 may be used to process the sensor information inorder to identify the construction objects and map a construction zone.Alternatively, this identification and classification may be performedby a computer of the vehicle that collected the sensor information, suchas computer 110 of vehicle 101 as described above. By using a computerof the vehicle to do the identifications and classifications ofconstruction zones in real time, this may increase the usefulness of theclassification as described in more detail below.

The area of the construction zone and the classification information maybe associated with one another and incorporated into detailed mapinformation for vehicles having an autonomous driving mode. Thus,computer 110 may store this information in the detailed map information.FIG. 6 includes detailed map information 500 having construction zoneareas 610 and 620 which may correspond to construction zone areas 910and 1010 of FIGS. 9 and 10 stored in the detailed map information 136.Again, FIG. 6 includes classifications 630 and 640, which may correspondto construction zones 910 and 1010, respectively. This information mayalso be sent to other vehicles in order to provide those vehicles withthe construction zone are and classification information. This may bedone by broadcasting the information to nearby vehicles having anautonomous driving mode or simply by sending it to a central server suchas server 720 which may relay the information to other vehicles havingan autonomous driving mode.

Alternatively, if the identification and classification of aconstruction zone is performed by a computer such as server 720, theconstruction zones and associated classifications may be transmitted toother autonomous vehicles and stored with their respective detailed mapinformation.

If the identification and classification of a construction zone isperformed by an autonomous vehicle such as vehicle 101, this informationmay be stored directly with vehicle 101's detailed map information Thisinformation may also be shared with other vehicles in order to updatetheir own detailed maps, for example, by sending it directly to specificvehicles, a broadcast to all other autonomous vehicles, or by sending itto a computer such as server 720 which may then send it to otherautonomous vehicles.

At some later time, a second vehicle, such as vehicle 101, operating inan autonomous driving mode may detect a construction zone. This may bedone by identifying a first construction object commonly associated witha construction zone as described above. Computer 110 of vehicle 101 maythen query the detailed map information to determine whether there is aconstruction zone associated with the location of the identified firstconstruction object. If not, computer 110 may proceed by mapping theconstruction zone, classifying the construction zone, storing thisinformation with the detailed map information, and, in some examples,sending it to other autonomous vehicle as described above.

If the location of the identified first construction object isassociated with the detailed map information, the computer 110 may alsoidentify the classification associated with that construction zone anduse the classification to determine how to drive the autonomous vehicle.Using the example above, when the construction zone is classified asactive, the second vehicle may user a higher level of caution, bydriving very slowly and perhaps transitioning from an autonomous to amanual mode. Similarly, when the construction zone is classified asinactive, the vehicle may continue to drive normally based on thedetailed map information.

Computer 110 may also determine whether it becomes necessary toreclassify a construction zone, for example by changing theclassification of a construction zone from active to inactive orinactive to active based upon the features of the roadway and thedetailed map information as described above.

Flow diagram 1100 of FIG. 11 is an example of some of the aspectsdescribed above which may be performed all or in part by computer 110 ofvehicle 101. In this example, the computer identifies a firstconstruction object associated with a construction zone at block 1102.An area of the construction zone is mapped based on the identifiedconstruction object at block 1104. The computer then classifies theactivity of the construction zone based on detailed map information atblock 1106. For example, a construction zones may be classified asactive or inactive. The area of the construction zone and theclassification are added to the detailed map information at block 1108.

Subsequent to adding the construction zone and the classification to thedetailed map information, the computer identifies a second constructionobject at a given location at block 1110. The computer then identifiesfrom the detailed map information, the area of the construction zone andthe classification based on the given location at block 1112. Thecomputer then operates a vehicle having an autonomous driving mode basedon the classification stored in the detailed map information at block1114.

In an alternative, rather than simply classifying a construction zone asactive or inactive, other classifications schemes may be used. Forexample, the classifications may be associated with or simply bedifferent pieces of information regarding how the vehicle shouldrespond. As an example, some construction zones may have a timecomponent, such as where road work on a highway is performed only duringnight hours (9:00 pm to 5:00 am, etc.). Thus, the vehicle need only bedriven manually or extra cautiously close to these hours. In anotherexample, there may be ongoing construction which only sometimes affectsa particular lane. This is example, the computer 110 would need to knowwhich lane is affected and what the computer 110 should look for whenapproaching the area, such as whether there are any cones near theparticular lane, the computer 110 can be highly confident that theparticular lane is in fact affected or closed. In another example, ifthe driving lanes are not affected by a construction project, but thereis active work occurring close to the lanes on the shoulder, it may besafer to drive the vehicle slowly through the area of the constructionzone, though not necessary. These details may also be associated with orsimply used to classify the construction zone.

The features described above allow a vehicle having an autonomousdriving mode to transition into another driving mode when there is areal need for it, but also to drive normally when there is not. As notedabove without the classifications, the vehicle would always have totransition to the manual mode when it detects a construction object inorder to promote a safe driving experience, even where it would not benecessary, such as in the example of construction zone area 1010/620.However, as noted above, because of the classification features providedabove, the autonomous vehicle may continue in the autonomous mode. Thus,the classifications allow the vehicle's computer to make a much moreinformed decision about how to operate in a construction zone.

As these and other variations and combinations of the features discussedabove can be utilized without departing from the subject matter asdefined by the claims, the foregoing description of exemplaryembodiments should be taken by way of illustration rather than by way oflimitation of the subject matter as defined by the claims. It will alsobe understood that the provision of the examples described herein (aswell as clauses phrased as “such as,” “e.g.”, “including” and the like)should not be interpreted as limiting the claimed subject matter to thespecific examples; rather, the examples are intended to illustrate onlysome of many possible aspects.

1. A method for operating a vehicle having an autonomous driving mode,the method comprising: classifying, by one or more processors, anactivity type of a construction zone based on characteristics of theconstruction zone; updating, by the one or more processors, a firstclassification for the construction zone in map information to a secondclassification based on the activity type; and operating, at an area ofthe construction zone, the vehicle, by the one or more processors, basedon the second classification.
 2. The method of claim 1, wherein thefirst classification is different from the second classification.
 3. Themethod of claim 1, further comprising determining whether the firstclassification should be updated based on the activity type, and whereinupdating the first classification is based on the determination.
 4. Themethod of claim 1, further comprising receiving sensor data identifyingfeatures in an environment of the vehicle, and wherein the classifyingincludes comparing the map information to the identified features. 5.The method of claim 4, wherein the features include a lane line and alocation of the lane line.
 6. The method of claim 4, wherein theidentified features include a closed lane.
 7. The method of claim 4,wherein the identified features include a barrier.
 8. The method ofclaim 4, wherein the identified features do not include constructionobjects.
 9. The method of claim 4, further comprising determiningwhether there is a corresponding feature in the map information, andwherein the classifying is further based on the determination.
 10. Themethod of claim 4, further comprising using the comparison to identifydifferences between any of the identified features and the mapinformation, and wherein the classifying is further based on theidentified differences.
 11. The method of claim 10, wherein thedifferences correspond to identified features other than constructionobjects.
 12. The method of claim 11, further comprising determiningwhether the identified differences would impact autonomous driving ofthe vehicle, and wherein the determination of whether the identifieddifferences would impact autonomous driving of the vehicle is used toclassify the activity type.
 13. The method of claim 12, wherein when theidentified differences are determined to not impact the autonomousdriving of the vehicle, the activity type corresponds to an inactiveactivity type.
 14. The method of claim 1, wherein when the activity typecorresponds to an active activity type, operating the vehicle includesdecreasing a speed of the vehicle.
 15. The method of claim 1, whereinwhen the activity type corresponds to an active activity type, operatingthe vehicle includes using caution when moving through the area of theconstruction zone.
 16. The method of claim 1, wherein when the activitytype corresponds to an active activity type, operating the vehicleincludes transitioning the vehicle from the autonomous driving mode to amanual driving mode.
 17. The method of claim 1, wherein when theactivity type corresponds to an inactive activity type, operating thevehicle in a same way as the vehicle operates outside of the area of theconstruction zone.
 18. The method of claim 1, wherein the activity typeis classified as either active or inactive.
 19. A system for operating avehicle having an autonomous driving mode, the system comprising one ormore processors configured to: classify, by one or more processors, anactivity type of a construction zone based on characteristics of theconstruction zone; update a first classification for the constructionzone in map information to a second classification based on the activitytype; and operate at an area of the construction zone, the vehicle basedon the second classification.
 20. The system of claim 19, furthercomprising the vehicle.